Open Access   Article

Epileptic Electroencephalogram Classification Using Machine Learning Algorithms

T. Perumal Rani1 , Heren Chellam G.2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 36-41, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.3641

Online published on Sep 30, 2018

Copyright © T. Perumal Rani, Heren Chellam G. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: T. Perumal Rani, Heren Chellam G., “Epileptic Electroencephalogram Classification Using Machine Learning Algorithms”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.36-41, 2018.

MLA Style Citation: T. Perumal Rani, Heren Chellam G. "Epileptic Electroencephalogram Classification Using Machine Learning Algorithms." International Journal of Computer Sciences and Engineering 6.9 (2018): 36-41.

APA Style Citation: T. Perumal Rani, Heren Chellam G., (2018). Epileptic Electroencephalogram Classification Using Machine Learning Algorithms. International Journal of Computer Sciences and Engineering, 6(9), 36-41.

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Abstract

Epilepsy is disease which is caused due to neurological disorder of a brain. It may cause recurrent seizures. It can be detected with the EEG signals and records the activity of brain electrically. In this paper K-Nearest Neighbor, Random Forest and Naive Bayes algorithms are used for classification of Electroencephalogram (EEG) signal as epilepsy or normal signal. These Machine learning algorithms learn directly from the data by experience which is not interrupted manually. Supervised learning uses labeled data for training which maps the input to the corresponding output. It classified into two types such as classification and regression. Classification means prediction of output from the input to which class it relies on, such as boy or girl. Whereas Regression means prediction of output from the input but output is predicted as a real value like measurement of rainfall etc. Here Random Forest method performs the best classification other than that of KNN and Naïve Bayes.

Key-Words / Index Term

Classification, Electroencephalogram, Epilepsy, Machine Learning, Regression

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